EP2499592A1 - Systèmes et procédés permettant de détecter la présence d'un statut biologique à l'aide d'un diagramme - Google Patents

Systèmes et procédés permettant de détecter la présence d'un statut biologique à l'aide d'un diagramme

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Publication number
EP2499592A1
EP2499592A1 EP10779420A EP10779420A EP2499592A1 EP 2499592 A1 EP2499592 A1 EP 2499592A1 EP 10779420 A EP10779420 A EP 10779420A EP 10779420 A EP10779420 A EP 10779420A EP 2499592 A1 EP2499592 A1 EP 2499592A1
Authority
EP
European Patent Office
Prior art keywords
sample
staphylococcus aureus
sccmec
meca
target gene
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP10779420A
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German (de)
English (en)
Inventor
Jiuliu Lu
Zihua Wang
Antonio Arevalo Reyes
Erik Alan Gustafson
John Steven Riley
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Beckman Coulter Inc
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Beckman Coulter Inc
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Filing date
Publication date
Application filed by Beckman Coulter Inc filed Critical Beckman Coulter Inc
Publication of EP2499592A1 publication Critical patent/EP2499592A1/fr
Withdrawn legal-status Critical Current

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B45/00—ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/20—Drawing from basic elements, e.g. lines or circles
    • G06T11/206—Drawing of charts or graphs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • MRSA A Staphylococcus aureus
  • MRSA methicillin-sensitive Staphylococcus aureus
  • MR-CoNS methicillin-resistant coagulase-negative staphylococci
  • MS-CoNS methicillin-sensitive coagulase-negative staphylococci
  • MRSA methicillin resistant Staphylococcus aureus
  • MSSA methicillin sensitive Staphylococcus aureus
  • One such method targets two separate regions of MRSA, the mecA gene of the Staphylococcus cassette chromosome (SCCmec, responsible for methicillin resistance) and spa gene of Staphylococcus aureus (United States Patent 5,702,895, Sinsimer, et al, Journal of Clinical Microbiology, Sept. 2005, 4585-4591).
  • SCCmec is a mobile genetic element that carries the mecA gene and inserts at a specific site, attBscc, at the 3 '-end of the orfX gene.
  • SCCmec The left extremity of SCCmec is contiguous with the non-orfX side of attBscc while the right extremity of SCCmec is contiguous with the orfX side of attBscc (Ito, et al., Antimicrob. Agent Chemother. 2001, 45, pl323-1336; Ito et al., Antimicrob. Agent Chemother. 2004, 48, p2637-2651, Noto, et al., J. Bacteriol. 2008, 190: 1276-1283).
  • This approach infers the presence of the mecA gene from the detection of the SCCmec/orfX junction. This approach requires the use of multiple primers as there have been several different types of SCCmec described.
  • Another approach utilizes one primer in a region of high homology across the different SCCmec types and one primer in the flanking Staphylococcus aureus DNA (Cuny, et al. Clin. Microbiol Infect 2005; 11 :834-837, European Patent 1529847 Bl). This approach is also subject to false positives as the probability of also priming of MSSA is high with primers encompassing this region.
  • Embodiments of the present invention relate to systems and methods for detecting methicillin resistant Staphylococcus aureus (MRSA) in a sample which may additionally contain methicillin sensitive Staphylococcus aureus (MSSA), methicillin resistant coagulase- negative staphylococci (MR-CoNS), and/or other strains of bacteria.
  • MRSA methicillin resistant Staphylococcus aureus
  • MSSA methicillin sensitive Staphylococcus aureus
  • MR-CoNS methicillin resistant coagulase- negative staphylococci
  • Other embodiments of the invention may relate more generically to systems and methods for the analysis of biological entities using plots and boundary functions.
  • One embodiment of the invention is directed to a method for determining the presence of a biological entity in a sample.
  • the method comprises detecting amounts of at least three genetic elements in the sample.
  • the presence of the biological entity in the sample is determined by executing a call algorithm on a digital computer.
  • the call algorithm uses as inputs the measured values (e.g., amounts) associated with at least three genetic elements and combines them to form a vector, which is compared to a boundary function on a 2- dimensional plot. If the vector is within the boundary function, the biological entity is present and if the vector is not within the boundary function, the biological entity is not present.
  • Another embodiment of the invention is directed to a system for determining the presence of a biological entity in a sample.
  • the system comprises a measurement module capable detecting the values (e.g., amounts) of at least three genetic elements in the sample, and a memory storing the detected values (e.g., amounts) from the measurement module.
  • the system also includes a computer readable medium containing computer readable code having instructions for executing a call algorithm, wherein the call algorithm uses as inputs the detected and measured values (e.g., amounts) of the at least three genetic elements and combines them to form a vector. The vector is compared to a boundary function on a 2- dimensional plot.
  • the system also includes a processor to execute the computer readable code on the computer readable medium in order to determine the presence of the biological entity in the sample.
  • Another embodiment of the invention is directed to a computer readable medium comprising code for a call algorithm.
  • the call algorithm uses as inputs detected and measured values (e.g., amounts) associated with the at least three genetic elements and combines them to form a vector.
  • the vector is compared to a boundary function on a 2- dimensional plot. If the vector is within the boundary function, a biological entity is present and if the vector is not within the boundary function, the biological entity is not present.
  • Another embodiment of the invention is directed to a method for creating a model that can be used to determine the presence of a biological entity in an unknown sample.
  • the method includes detecting the presence and values (e.g., amounts) of at least three genetic elements in the known samples and executing a call algorithm on a digital computer for each sample in known samples, wherein the call algorithm creates vectors and uses as inputs for each vector the detected values (e.g., amounts) of the at least three genetic elements. Some vectors are associated with the biological entity and some vectors are not associated with the biological entity.
  • the method further includes plotting the vectors on a 2-dimensional plot, and creating a boundary function separating the vectors that are associated with the biological entities and the vectors that are not associated with the biological entity.
  • a method for determining the presence of methicillin-resistant Staphylococcus aureus (MRSA) in a sample is disclosed.
  • the method subjects the sample to conditions that will expose the nucleic acids of any bacteria present in the sample.
  • the sample may be amplified and the presence and amounts of at least mecA, SCCmec, and a Staphylococcus aureus (SA)-specific target gene sequence in the sample can be detected.
  • SA Staphylococcus aureus
  • the call algorithm may use as inputs the detected and measured amounts of mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence to determine whether MRSA is present in the sample.
  • the call algorithm can be used to determine that MRSA is present in the sample when the detected amounts of mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence in the sample are approximately equal as defined by a selected boundary function.
  • the system may comprise a number of components.
  • the system may comprise a
  • the system may further comprise a memory that can store the detected amounts from the measurement module.
  • the system may also comprise a computer readable medium containing computer readable code having instructions for executing a call algorithm. The call algorithm may use as inputs the detected and measured amounts of mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence to determine whether MRSA is present in the sample.
  • the call algorithm can be used to determine that MRSA is present in the sample when the detected amounts of mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence in the sample are approximately equal as defined by a selected boundary function.
  • the system may also comprise a processor that can execute the computer readable code on the computer readable medium in order to determine the presence MRSA in the sample.
  • a computer-readable medium is disclosed.
  • the computer-readable medium may comprise code for a call algorithm that can determine the presence of MRSA in a sample.
  • the call algorithm may use as inputs detected and measured amounts of mecA, SCCmec, and a Staphylococcus aureus-specific target gene sequence to determine whether MRSA is present in the sample.
  • the call algorithm can be used to determine that MRSA is present in the sample when the detected amounts of mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence in the sample are approximately equal as defined by a selected boundary function.
  • the detected and measured amounts of mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence can be obtained by subjecting the sample to conditions that will expose the nucleic acids of any bacteria present in the sample and amplifying and detecting the presence and amounts of at least mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence in the sample.
  • a method for creating a model that can be used to determine the presence of methicillin-resistant Staphylococcus aureus (MRSA) in an unknown sample may comprise subjecting a set of known samples to conditions that will expose the nucleic acids of any bacteria present in the known samples. The presence or absence of MRS A is known for each sample in the set of known samples. The method may then amplify and detect the presence and amounts of at least mecA, SCCmec, and a Staphylococcus aureus-specific target gene sequence in the known samples. The method may then execute a call algorithm on a digital computer for each sample in the known samples.
  • MRSA methicillin-resistant Staphylococcus aureus
  • the call algorithm may use as inputs the detected and measured amounts of mecA, SCCmec, and the Staphylococcus aureus-specific target gene sequence.
  • the method may create a model that can be used to determine whether MRS A is present in the unknown sample.
  • the model can be created from the output of the call algorithm executed against the known samples, and the model can be defined by a selected boundary function that defines a boundary between MRSA-positive and MRSA-negative samples.
  • FIG. 1 shows a flowchart illustrating the steps taken in a method according to one embodiment.
  • Fig. 2 shows a flowchart illustrating the steps taken in a method according to one embodiment of the invention.
  • Figs. 3-5 show diagrams illustrating the outputs of call algorithms from various positive and negative samples according to embodiments of the invention.
  • Fig. 6 is a block diagram of a system that can be used to execute various embodiments of the invention.
  • Various embodiments disclose systems and methods for identifying Methicillin resistant strains of Staphylococcus aureus (MRSA) in a sample that are based on the fact that an MRSA-positive sample can have roughly the same copy numbers of mecA, SCCmec, and a Staphylococcus aureus-specific target gene sequence. These systems and methods may further present the three assays simultaneously on a 2-D plot with each axis of the plot 120° angle apart. According to one embodiment, a Y plot is used for the 2-D display. If a given sample has similar values of mecA, SCCmec, and a Staphylococcus aureus-specific target gene sequence, the sample's measured copy numbers of mecA, SCCmec, and the
  • Staphylococcus aureus-specific target gene sequence can plot very close to the origin regardless of the sample's absolute assay readings.
  • a boundary function can be defined that can be used to distinguish MRSA-positive samples from MRSA-negative samples.
  • a "boundary function” can be a mathematical function that is used to determine whether data is associated with a biological status or is not associated with a biological status. Boundary functions may be created in any suitable manner including manually, by the use of neural networks, cost functions, etc. Boundary functions may also be represented by any suitable shape or line, including an ellipse, rectangle, circle, or the like. Boundary functions may also be regular or irregular in shape. [0028] As used herein, a "vector" can relate to value derived from one or more
  • each component having at least a direction, and typically a scalar.
  • genetic element can refer to a subsequence in a genome of interest that is useful as a target in the methods of the invention.
  • the genetic element is an open reading frame or gene, such as, for example, orfX, femA or mecA in Staphylococcus.
  • a genetic element may also be a mobile genetic element, such as the Staphylococcus cassette chromosome, SCCmec, which may or may not comprise the mecA gene.
  • SCCmec SCCmec sequence
  • SCCmec cassette the genetic element known as the Staphylococcus cassette chromosome, which carries the mecA gene and is inserted into Staphylococcus sp. genome as described in Ito et al. (2001, Antimicrob. Agents Chemother. 45:1323-1336).
  • the SCCmec insertion site is referred to as "orfX-ISS/at Bscc” in this application.
  • the insertion site is at the 3' end of a gene referred to herein, as “orfX.”
  • the chromosomal locus where SCCmec insertion takes place is reffered to as “attBscc.”
  • the specific sequence at the insertion site is referred to here as the “orfX-Insertion Site Sequence (orfX-ISS)" or "attBscc core region.” This sequence is known to be a highly conserved sequence in Staphylococcus aureus (Ito, et al., Antomicrob. Agent Chemother. 2001, 45, p323-1336, Noto, et al., J. Bacteriol. 2008, 190:1276-1283).
  • the SCCmec left extremity junction region is referred to as MRSA-LE and the right extremity junction region is referred to as MRSA-RE.
  • the SCCmec sequence is contiguous with the non-orfX side of attBscc.
  • the SCCmec sequence is contiguous with the orfX-side of attBscc.
  • the orfX-ISS/attBscc region is described in detail in Ito et al. (2001 , Antimicrob. Agents Chemother.
  • oligonucleotide is a nucleotide polymer having two or more nucleotide subunits covalently joined together. Oligonucleotides are generally about 10 to about 100 nucleotides.
  • the sugar groups of the nucleotide subunits may be ribose, deoxyribose, or modified derivatives thereof such as OMe.
  • the nucleotide subunits may be joined by linkages such as phosphodiester linkages, modified linkages or by non-nucleotide moieties that do not prevent hybridization of the oligonucleotide to its complementary target nucleotide sequence. Modified linkages include those in which a standard phosphodiester linkage is replaced with a different linkage, such as a phosphorothioate linkage, a
  • a “target nucleic acid” is a nucleic acid comprising a target nucleic acid sequence.
  • a “target nucleic acid sequence,” “target nucleotide sequence” or “target sequence” is a specific deoxyribonucleotide or ribonucleotide sequence that can be hybridized to a complementary oligonucleotide.
  • probe refers to an oligonucleotide which is capable of hybridizing to a target nucleic acid of interest. The hybridization occurs as a result of the probe binding through complementary base pairing to a target nucleic acid of interest. It will be understood by one skilled in the art that probes will typically substantially bind target sequences lacking complete complementarity with the probe sequence depending upon the stringency of the hybridization conditions.
  • the probe may be associated with a suitable label or reporter moiety so that the probe (and therefore its target) can be detected, visualized, measured and/or quantitated.
  • primer refers to an oligonucleotide used to prime nucleic acid synthesis.
  • a primer hybridizes to the template through complementary base pairing and is therefore used to initiate the replication. Hybridization occurs in the same manner as that described for probes, above. In PCR, two primers are used: a "forward primer” that typically hybridizes to the sense strand of a double stranded nucleic acid molecule and a “reverse primer” that typically hybridizes to the antisense strand of the molecule.
  • PCR refers to a technique for exponential amplification of short DNA sequences (usually 50 to 600 bases) within a longer double stranded DNA molecule by enzymatic replication of DNA without using a living organism (Mullis et al. Methods Enzymol. 1987; 155:335-50). Other in vitro amplification
  • LCR Ligase Chain Reaction
  • NASBA Nucleic Acids Sequence Based Amplification
  • SDA Strand Displacement Amplification
  • TMA Transcription Mediated Amplification
  • bDNA Branched DNA technology
  • RCAT Rolling Circle Amplification Technology
  • Real-Time PCR refers to a type of PCR where the amplified DNA is quantified as it accumulates in the reaction in real time after each amplification cycle (Heid et al, Genome Research, 1996 6(10):986-994).
  • a number of probe chemistries for carrying out Real-Time PCR are well known to those of skill.
  • One commonly used method is the the TaqMan® assay ⁇ see, e.g., U.S. Pat. Nos. 5,210,015; 5,487,972; and 5,804,375).
  • Real-Time PCR probe chemistries that can be used and can be purchased commercially include FRET primers, Molecular Beacons, Scorpion primers®, Amplifluor primers®, LUX primers®, Eclipse®, and Ultimate Probe®.
  • FRET primers Fluorescence PCR
  • Molecular Beacons Molecular Beacons
  • Scorpion primers® Amplifluor primers®
  • LUX primers® Eclipse®
  • Ultimate Probe® For a review of Real-Time PCR techniques see Bustin et al., J. Mol. Endocrin. 34:597-601 (2005).
  • the term “Multiplex PCR” refers to a type of PCR where more than one set of primers is included in a reaction allowing two or more different targets to be amplified in a single reaction tube.
  • multiplex PCR also refers to a PCR where multiple primers and probes are used but only one target is amplified.
  • the multiplex PCR of the present invention is a real-time
  • a "biological status" may relate to a particular biological state of a sample derived from a patient. In most cases, the biological status relates to whether or not the sample comprises a particular biological entity, for example, a target disease organism or patient cell associated with disease. For example, one biological status may be that a sample comprises MRSA bacteria, while another biological status may be that the sample does not comprise MRSA bacteria. In other examples, the biological status may relate to whether or not the sample comprises cancer cells. [0040] One embodiment of the invention relates to an assay for detection of MRSA in a sample that may contain MRSA, MSSA, MR-CoNS, or other bacteria. Embodiments of the invention utilize a multiplex PCR for simultaneously amplifying and detecting a combination of multiple genetic elements (e.g., targets).
  • the initial amount of target DNA is measured by the PCR threshold cycle (Ct). For example, a defined signal threshold is determined for a reaction to be analyzed. The number of cycles (Ct) required to reach this signal threshold is determined for the target nucleic acid as well as for a reference or standard nucleic acid. The absolute or relative copy numbers of the target molecule can be determined on the basis of the Ct values obtained for the target nucleic acid as compared to the reference nucleic acid. The Ct value is thus inversely proportional to the amount of initial target DNA, see Heid et al, 1996, Genome Research 6(10):986 for a full discussion of the Ct value which is incorporated herein by reference. Other mathematical approaches can be employed which allow for the extrapolation of the initial amount of a particular target gene based upon the indication of a predetermined set amount or number of genes amplified during one of the identified methods.
  • the present invention is directed to a method of determining the presence of MRSA in a sample, said method comprising subjecting the sample to real-time PCR for a time and under conditions so as to generate a level of amplification product which is sufficient to be detected by fluorescence and is indicative of an initial level of one or more MRS A- specific target sequences in the sample.
  • the amplification is conducted with a set of primers (forward and reverse) and a probe.
  • the probe may be labeled with a fluorogenic reporter molecule at its 5' end and a quenching molecule at its 3' end.
  • the quenching molecule prevents emission of signal from the fluorogenic reporter molecule.
  • the probe hybridizes to a region of the target sequence between the regions to which the forward and reverse primers hybridize. As the polymerase moves along the strand to which the probe has hybridized, the 5' end of the probe is cleaved off by the exonuclease activity of the polymerase thus permitting emission of the fluorogenic signal due to separation of the quenching moiety.
  • the probes of the invention may comprise dual-labeled fluorogenic probes comprising a fluorescent reporter (fluorophore) and a fluorescent or non- fluorescent quencher molecule.
  • the fluorophores of embodiments of the invention may be attached to the probe at any location, including the 5' terminus, the 3' terminus or internal to either termini.
  • the fluorophore and quencher are attached to the 5' and 3' termini of the probe respectively.
  • the examples of fluorophores include, but are not limited to, FAM, ROX, HEX, NED, Cy5, Texas Red, Calfluor Red, CalFluor Orange, Quasar 670, Quasar 705.
  • the examples of quenchers include, but are not limited to,
  • the invention provides a method for detecting and distinguishing MRSA from MSSA, MR-CoNS, or other bacteria utilizing a three target assay, wherein the targets used in the assay include the mecA gene sequence, a Staphylococcus aureus-specific target gene sequence, and an SCCmec gene sequence.
  • the Staphylococcus aureus-specific target gene is femA.
  • femA is often explicitly mentioned as the Staphylococcus aureus-specific target gene sequence; however, other Staphylococcus aureus-specific target gene sequences may also be used according to various embodiments.
  • Yet other targets that can be used to determine the presence of MRSA can include orfX.
  • Various embodiments need not use traditional positive-negative or delta approaches in order to determine the presence of MRSA. Instead, an approach is used that is based on the fact that an MRSA positive sample has roughly the same copy numbers of mecA, femA, and SCCmec. When the copy numbers of mecA, femA, and SCCmec are present in roughly equal amounts, it can be indicative of the presence of MRSA in the sample.
  • the Ct values of mecA, SCCmec, and femA can be analyzed using an assay call algorithm. This call algorithm may further present the three assays simultaneously in a 2-D plot with lines 120 degrees apart.
  • This plot can be referred to as a Y-plot since each of the three axes of the plot, representing the measured amounts of mecA, SCCmec, and femA, appear as a "Y" on a 2-D plot. If a sample has similar readings of mecA, femA, and
  • Fig. 1 illustrates steps that can be used to build a model that can be used to determine whether MRSA is present in a sample according to one embodiment.
  • a boundary function in the model can separate MRSA-positive samples from MRSA-negative samples based on the measured amounts of mecA, SCCmec, and a Staphylococcus aureus-specific target gene sequence.
  • one embodiment of the invention is directed to a method for creating a model that can be used to determine the presence of a biological entity in an unknown sample.
  • the method includes detecting the presence and amounts of at least three targets in the known samples and executing a call algorithm on a digital computer for each sample in known samples, wherein the call algorithm creates vectors and uses as inputs for each vector the detected amounts of the at least three targets. Some vectors are associated with the biological entity and some vectors are not associated with the biological entity.
  • the method further includes plotting the vectors on a 2-dimensional plot, and creating a boundary function separating the vectors that are associated with the biological entities and the vectors that are not associated with the biological entity.
  • a selected number of known samples are subjected to conditions that expose the nucleic acids of bacteria in the samples.
  • a known sample is a sample in which it is already known whether the sample should test positive or negative for MRSA.
  • the known samples can thus be used to build a model that can determine whether a later unknown sample also contains MRSA.
  • MRSA is described in detail, it is understood that the presence of other suitable biological entities could be detected in other embodiments of the invention.
  • a sample may have its temperature raised to separate strands of DNA.
  • Other well-known means for exposing the nucleic acids in a sample may also be used.
  • the presence and amounts of at least three targets are detected for each of the known samples.
  • Ct values may be measured for each of the targets of interest.
  • the Staphylococcus aureus-specific target gene sequence is femA.
  • a multiplex PCR can be used to measure the PCR threshold cycle (Ct) for each target of the measurement. Other measurement techniques may also be used.
  • the determined Ct values can be used to create a Y-Plot.
  • a pre-screening process can be carried out prior to the creation of the Y-Plot.
  • the sample is deemed MRSA negative and will not be shown in the Y-Plot.
  • a sample is deemed negative if mecA > 35, femA > 32, or SCCmec > 32.
  • a sample is deemed negative if mecA > 30, femA > 30, or SCCmeO 30.
  • the sample will be plotted on and analyzed with the Y-Plot after it survives this prescreening process.
  • the thresholds used in the prescreening process can be dependent on instrument and sample preparation.
  • a call algorithm can be applied to each of the measured targets from each of the known samples.
  • the call algorithm may use a Y plot.
  • the call algorithm creates an intermediate vector that can be projected onto a 2-D plot, which may be a Y_plot.
  • the intermediate vector may be the sum of three unit vectors 120 degrees apart from each other and weighted by the mecA, femA, and SCCmec values taken from step 1020.
  • the sum of the vectors can be represented by the formula: , where p is the intermediate vector. femA may
  • SA (representing a value associated with a
  • Staphylococcus aureus-specific target gene sequence Other similar means for representing how the relative amounts of the measured targets may also be used according to other embodiments.
  • the intermediate vectors created during step 1020 for each known sample can be analyzed in order to create a model that can be used to determine whether unknown samples contain MRSA.
  • a boundary function is selected and the parameters of the boundary function are defined using the intermediate vectors of the known samples.
  • the boundary function can be used to determine if MRSA is in an unknown sample or is not in an unknown sample.
  • a clustering analysis can be performed on a Y plot of all of the measured known samples to separate MRSA positive and negative samples.
  • a clustering analysis can group positive samples together because femA, mecA, and SCCmec are single copy targets and there should be equal copies of these targets in MRSA positive samples.
  • a simple boundary function such as a rectangular area that encompasses the positive samples, can be used to cluster the positive samples that are located around the origin of the Y plot.
  • the negative samples may be scattered outside of this boundary.
  • a simple rectangular box can be used to separate positive and negative samples on the Y plot.
  • the boundaries of the rectangular box can be adjusted using the measurements from the known samples.
  • a neural network algorithm could be used to help define the boundaries of the box.
  • Other embodiments may select more complicated boundary functions or use other means to adjust the coefficients of the selected boundary function.
  • boundary functions can be created using other approaches (e.g., a circular gating process, a neural network, or a Gaussian distribution function).
  • Fig. 2 illustrates steps that can be used to determine whether MRSA is present in a sample according to one embodiment.
  • an unknown sample refers to a sample in which it is not known whether MRSA is present in the sample.
  • the steps in Fig. 2 can use a model, such as a model created using the steps from Fig. 1, to determine whether an unknown sample contains MRSA.
  • the unknown sample can have various targets measured, and these measurements can be used to detect the presence of MRSA by analyzing where an intermediate vector created from the measured targets of the unknown sample resides relative to a boundary function of the model.
  • an embodiment of the invention is directed to a method for determining the presence of a biological entity in a sample.
  • the method comprises detecting values (e.g., amounts) of at least three targets in the sample.
  • the presence of the biological entity in the sample is determined by executing a call algorithm on a digital computer.
  • the call algorithm uses as inputs the measured values (e.g., amounts) of at least three targets and combines them to form a vector, which is compared to a boundary function on a 2- dimensional plot. If the vector is within the boundary function, the biological entity is present and if the vector is not within the boundary function, the biological entity is not present.
  • the unknown sample is subjected to conditions that expose the nucleic acids of bacteria in the sample. The same techniques used during step 1000 while building the model can also be used at step 1100.
  • the Ct values at least three targets, mecA, SCCmec, and a
  • Staphylococcus aureus-specific target gene sequence are measured from the unknown sample. The same techniques used during step 1010 can be used to accomplish step 1110.
  • the call algorithm used to build the model can be applied to the measured targets from unknown sample.
  • an intermediate vector can be created from the unknown sample using the same function that was used at step 1020.
  • step 1130 it is determined whether MRSA is present in the unknown sample. According to one embodiment, this can be accomplished by comparing the intermediate vector created for the unknown sample against the boundary function created from the known samples. If the intermediate vector for the unknown sample falls within the boundary that designates positive MRSA samples, then it can be determined that MRSA is present in the unknown sample. If the intermediate vector for the unknown sample falls outside of the positive MRSA boundary, then it can be determined that MRSA is not present in the unknown sample. [0064] Embodiments of the invention are particularly advantageous. As noted below, embodiments of the invention are particularly useful in identifying MRSA in a sample, despite the fact that MRSA can often co-colonize with multiple other related bacteria.
  • samples were collected and analyzed to form a model that can be used according to one embodiment.
  • the samples were known samples, i.e., it was known whether MRSA was present in each of the samples.
  • Two swabs were collected for each sample. These two swabs were then cultured for MRS A independently. If the culture results from two swabs agree, this sample was considered valid.
  • Each valid known sample was then placed in a sample buffer tube with 1ml of Tris, pH 8.0 and ImM EDTA, pH8.0.
  • the sample buffer tube was vortexed for 40 seconds at 3000 rpm to dislodge bacteria from the swab head.
  • 500 ⁇ , of the bacterial suspension i.e., the sample buffer containing bacterial suspension was then transferred into a new tube.
  • a pre-mix was then prepared by mixing together 188 ⁇ , of lysis buffer, 1.0 ⁇ of polyA RNA (10 ⁇ ), and ⁇ of Proteinase K (20 mg/ml) (in 10 mM Tris pH 8.0, 50% glycerol, 5 mM calcium chloride).
  • the pre-mix was then added to the tubes, vortexed for 10 seconds at 1700 RPM, and then incubated in a 70°C water bath for 5 minutes.
  • the tubes were then incubated at room temperature for 2 minutes and then placed on a microcentrifuge tube magnet for 6 minutes to capture the magnetic beads. The supernatant was aspirated off without disturbing the magnetic beads. The tubes were then taken off the magnet and 500 ⁇ , of Wash Buffer (3.3M Guanidine thiocyanate, 1.7% v/v Triton X-100, 167.5 mM sodium citrate) was added.
  • Wash Buffer 3.3M Guanidine thiocyanate, 1.7% v/v Triton X-100, 167.5 mM sodium citrate
  • PCR reagents were first thawed and then maintained on ice during the following steps. Edge wells were not used on a 96-well plate (Rows A and H, columns 1 and 12). One positive and 1 negative control were used per PCR plate. [0079] A reaction mix was then prepared for the required quantity of samples and control. The reaction mix was prepared based on increments of 12 reactions.
  • reaction mix was aliquoted into a Stratagene 96-well PCR plate at a volume of 30 ⁇ . per well.
  • 20 ⁇ _, of SPRI-TE extracted DNA sample was added to a single well.
  • 20 ⁇ , of Negative Control was added to a single well with nuclease free water.
  • 20 ⁇ ⁇ of Positive Control was added to a single well.
  • the sample and reaction mix was then created by pipeting the mixture up and down 10 times, using a pipette set at 45 ⁇ ,.
  • the prepared PCR plate was then sealed with an Optical Adhesive Cover and then centrifuged at 3000 rpm for 3 minutes.
  • the plate was then loaded onto a Stratagene Mx3005P qPCR instrument.
  • the used wells in the plate were selected and the following dyes were selected for fluoresence data collection: CY5, HEX, ROX, FAM.
  • the following cycling conditions were specified: 4' @ 37°C (lx); V @ 95°C (lx); 5" @ 91°C ⁇ >10" @62°C -> 25" @ 58°C (50x).
  • the probes for SCCmec have sequences that are complementary to the right extremity of SCCmec.
  • primers and probes that are substantially identical in sequence and/or length can be used in embodiments of the invention.
  • each intermediate vector was the sum of three unit vectors 120 degrees apart from each other and weighted by mecA, femA, and SCCmec values respectively.
  • the formula used was: , where p is the intermediate
  • the intermediate vectors created from the MRS A positive samples generally have their endpoints clustered around the origin of the plot.
  • the MRSA negative samples generally have the endpoint of their intermediate vectors residing at locations more distant from the origin.
  • Fig. 4 shows how a simple rectangle box 4000 can be used as a boundary function to separate positive and negative samples.
  • the left, right, top, and bottom edges of the rectangle box are at -3, 2.5, 2, and -1.3.
  • the boundary may be defined by a function that is then tuned using a neural network.
  • the boundary does not need to rectangular; more complicated geometries may yield better fits to the known samples in other models.
  • the known samples can then be analyzed using the newly created model to test how accurately the model categorizes the known samples.
  • a Y-plot-based call algorithm that computes an intermediate vector for each known sample and compares the vector with the boundary function, as described above, resulted in 5 false negatives (FN's) and 0 false positives (FP's) for the 659 samples.
  • FN's false negatives
  • FP's false positives
  • One of these false negatives is highlighted in Fig. 4 at 4010.
  • the swab heads were then sterilely removed from the sample tubes and transferred into 15 ml bacteria culture tubes with 1 ml of Trypic Soy broth (TSB) and 6.5% NaCl.
  • TTB Trypic Soy broth
  • the inoculated bacteria tubes were transferred into a 37°C incubator and incubated overnight with shaking at speed of 200 rpm.
  • CHROMagar MRSA and a BBLTM CHROMagar Staphylococcus aureus plate 500 ⁇ , of the 1200 ⁇ L ⁇ sample solution from each tube was then subjected to DNA isolation procedure as described by Agencourt VirNA kit protocol. This procedure, in brief, began with an amount of CFU S. felis bacteria (or an amount of S. felis without CFU) as a process control. 10 units of Achrompeptidase were added to each tube, mixed well, and incubated in a 70 °C waterbath for 4 minutes. 289 iL of a freshly prepared lysis solution containing 188 ⁇ , of a lysis buffer, 1.0 ⁇ .
  • the supernatant was aspirated off the samples while being careful not to remove any beads during aspiration.
  • 500 ⁇ , of washing buffer were added to the samples and vortexed for 10 seconds to mix.
  • the tubes were then incubated on the magnet for 4 minutes (or until clear).
  • the supernatant was then aspirated off the samples again.
  • 900 ⁇ , of freshly prepared 75% ethanol was then added and the tube vortexed for 10 seconds.
  • the tubes were then incubated on the magnet for 4 minutes until clear.
  • the supernatant was then aspirated off the samples again and the ethanol washing was repeated one more time.
  • the beads were then dried on the magnet for 15-25 minutes. When the ring of the beads started to crack, the sample was eluted.
  • the tubes were taken off the magnet and 25 ⁇ , of nuclease free water was added to elute the DNA. The samples were then vortexed to mix. The tubes were then incubated for 5 minutes at 70°C. The tubes were placed back on the magnet and incubated for 1 minute. The eluate was then transferred to a clean tube for PCR
  • the reagents listed in the Master mix table were prepared on ice. According to the total reaction number, enough Master mix can be prepared by simply adding the indicated volumes of reagents together in a DNA/RNase-free tube. The tubes can be vortexed to mix and then left on ice for later use. 20 ⁇ , of each eluate was added to a Mx3000P 96-well PCR plate (non-skirted) (Stratagene, Cat#401333) (one eluate, one well). 30 ⁇ , Master mix was added to each well filled with the eluate, and then mixed by gently pipetting up and down 8 times or more (a multi-channel might be useful.). The plate was covered tightly with
  • Model creation and analysis [0102] The threshold values of each target are exported and input into a call algorithm as previously described with the first illustrative example.
  • Fig. 6 is a block diagram of a computer system 300 that can be used to execute one embodiment of the invention.
  • the computer system 300 has a number of input modules.
  • a measurement module 301 is used to measure selected targets in a sample. This measurement module may vary between different embodiments of the invention depending on the measurement method selected to measure the target responses. For example, according to one embodiment, the measurement module may conduct a PCR analysis on a sample.
  • the measurement module may be embodied by at least part of the working components of a typical real time PCR apparatus. Also shown are a standard keyboard 302 and mouse 303.
  • the computer system 300 also contains a variety of typical computer components inside the computer. These components include a system bus 304, one or more disk drives 305, RAM
  • Fig. 5 also shows a monitor 308 that allows information to be displayed to a user of the system.
  • a sample is placed in the measurement module 301 where the sample is processed and amounts of the selected targets from the sample are measured. This information is then transferred into the computer system along a system bus 304, and an appropriate call algorithm can be applied to the response data using the processor
  • the instructions the processor 307 executes to implement the call algorithm are stored on a computer readable medium such as the RAM 306 or disk drive 305.
  • the call algorithm can also be stored on this same media.
  • the output of the call algorithm can then be displayed on the monitor 308. For example, if the call algorithm uses a 2-D Y plot to graph the measured amounts of three targets via an intermediate vector, the end point of the call algorithm
  • intermediate vector can be displayed on the monitor 308.
  • Alternative embodiments of the invention can output information via other communications means.
  • the computer system could print the Y plot using a printer or send the Y plot to another computer over a network. The information from the measured sample can then be used to either help build a model or determine whether the sample contains MRS A.
  • the software components, steps, or functions described in this application may be implemented as software code to be executed by one or more processors using any suitable computer language such as, for example, Java, C++ or Perl using, for example, conventional or object-oriented techniques.
  • the software code may be stored as a series of instructions, or commands on a computer readable medium (e.g., a non-transitory computer readable medium), such as a random access memory (RAM), a read only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a CD-ROM.
  • a computer readable medium e.g., a non-transitory computer readable medium
  • RAM random access memory
  • ROM read only memory
  • magnetic medium such as a hard-drive or a floppy disk
  • optical medium such as a CD-ROM.
  • Any such computer readable medium may also reside on or within a single computational apparatus, and may be present on or within different computational apparatuses within a system or network
  • control logic in software or hardware or a combination of both.
  • the control logic may be stored in an information storage medium as a plurality of instructions adapted to direct an information processing device to perform a set of steps disclosed in an embodiment of the present invention.
  • MRSA Staphylococcus aureus

Abstract

La présente invention a pour objet des systèmes et des procédés permettant d'identifier des souches de Staphylococcus aureus résistantes à la méthicilline (SARM) dans un échantillon, qui sont basés sur le fait qu'un échantillon positif pour le SARM devrait contenir approximativement le même nombre de copies de mecA, SCCmec, et une séquence de gène cible spécifique de Staphylococcus aureus. Les systèmes et les procédés peuvent présenter en outre les trois dosages simultanément sur un diagramme en 2 dimensions, les deux axes du diagramme étant séparés de 120 °. Selon un mode de réalisation, un diagramme Y est utilisé pour l'affichage en 2 dimensions. Si un échantillon donné présente des déterminations similaires de mecA, SCCmec, et d'une séquence de gène cible spécifique de Staphylococcus aureus, les nombres de copies mesurés de l'échantillon de mecA, SCCmec, et de la séquence de gène cible spécifique de Staphylococcus aureus peuvent pointer graphiquement près de l'origine indépendamment des déterminations de dosage absolues de l'échantillon. A l'aide de cette transformation, une fonction limite qui peut être utilisée pour distinguer les échantillons positifs pour le SARM des échantillons négatifs pour le SARM peut être définie.
EP10779420A 2009-11-13 2010-11-11 Systèmes et procédés permettant de détecter la présence d'un statut biologique à l'aide d'un diagramme Withdrawn EP2499592A1 (fr)

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JP3957338B2 (ja) 1996-02-23 2007-08-15 株式会社カイノス 診断薬
DE60217837T2 (de) * 2001-03-15 2007-10-25 Jacques Schrenzel Nachweis von methicillin-resistenten staphylococcus aureus bakterien (mrsa)
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JP5907879B2 (ja) 2016-04-26
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